Life Time Data: Statistical Models and Methods

The targeted audience for this book is graduate students in engineering and medical statistics courses, and it may be useful for a senior undergraduate statistics course. To get the maximum benefit from this book, one should have a good knowledge and understanding of calculus and sufficient background in elementary probability theory to understand the central limit theorem and the law of large numbers. Some more sophisticated probability terminologies and concepts are defined for a smooth reading of the monograph. This monograph has 10 chapters, including the introduction. Chapter 2 deals with the ageing concept and some usual parametric families of probability distribution are presented in Chapter 3. Parametric and nonparametric statistical inference are nicely treated in Chapters 4 and 5. Chapter 5 also offers tests for exponentiality, which is one of the main feature of the monograph. Chapters 7 and 8 cover two-sample and regression problems, respectively. All of the preceding chapters showcase results for both complete and censored data. One of the interesting contributions is with regard to the analysis of competing risk, which is presented in Chapter 9. Finally, Chapter 10 introduces repairable systems. One of the main strengths of this book is that it introduces the public domain R software and nicely explains how it can be used in computations of methods presented in the book. This book has sufficient material and examples to cover a one semester (13week) course. However, I would be reluctant to adopt this book for one simple reason—there are no exercises. Having said that, the monograph would be useful to some applied researchers in related fields.